# Predicting Aesthetic Score Distribution through Cumulative Jensen-Shannon Divergence

arXiv: https://arxiv.org/abs/1708.07089 · HTML: https://mlsanity.com/paper/1708.07089/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Aug 2017.

## Built on by (most cited first, 9 of 9)

- [Beyond correlation: The Impact of Human Uncertainty in Measuring the Effectiveness of Automatic Evaluation and LLM-as-a-Judge](https://mlsanity.com/paper/2410.03775/) (2024)
- [Quality Assessment in the Era of Large Models: A Survey](https://mlsanity.com/paper/2409.00031/) (2024)
- [Provable Benefits of Policy Learning from Human Preferences in Contextual Bandit Problems](https://mlsanity.com/paper/2307.12975/) (2023)
- [User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning](https://mlsanity.com/paper/2106.07488/) (2021)
- [The Earth Mover's Pinball Loss: Quantiles for Histogram-Valued Regression](https://mlsanity.com/paper/2106.02051/) (2021)
- [A Deep Drift-Diffusion Model for Image Aesthetic Score Distribution Prediction](https://mlsanity.com/paper/2010.07661/) (2020)
- [AesTest: Measuring Aesthetic Intelligence from Perception to Production](https://mlsanity.com/paper/2511.06360/) (2025)
- [ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning](https://mlsanity.com/paper/2110.15522/) (2021)
- [Aesthetic Attributes Assessment of Images with AMANv2 and DPC-CaptionsV2](https://mlsanity.com/paper/2208.04522/) (2022)

## Abstract (arXiv)

Aesthetic quality prediction is a challenging task in the computer vision community because of the complex interplay with semantic contents and photographic technologies. Recent studies on the powerful deep learning based aesthetic quality assessment usually use a binary high-low label or a numerical score to represent the aesthetic quality. However the scalar representation cannot describe well the underlying varieties of the human perception of aesthetics. In this work, we propose to predict the aesthetic score distribution (i.e., a score distribution vector of the ordinal basic human ratings) using Deep Convolutional Neural Network (DCNN). Conventional DCNNs which aim to minimize the difference between the predicted scalar numbers or vectors and the ground truth cannot be directly used for the ordinal basic rating distribution. Thus, a novel CNN based on the Cumulative distribution with Jensen-Shannon divergence (CJS-CNN) is presented to predict the aesthetic score distribution of human ratings, with a new reliability-sensitive learning method based on the kurtosis of the score distribution, which eliminates the requirement of the original full data of human ratings (without normalization). Experimental results on large scale aesthetic dataset demonstrate the effectiveness of our introduced CJS-CNN in this task.